memor-cli 0.10.0

Measured memory for coding agents. Fire and forget — no API keys needed.

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  Measured memory and opt-in token savings for coding agents.

Automatic background memory for Claude Code, Cursor, Codex, Copilot, Kimi, and Goose — plus optional local token savings for Claude Code and Codex. Memory is fire-and-forget; proxy is opt-in. No Memor API key required.

Memor watches your coding sessions, extracts decisions and patterns, and recalls relevant context on every prompt. Optionally, a local proxy compresses tool payloads before they reach the provider and tracks measurable token savings on the dashboard.

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Quick Start

# Install globally (recommended)
pipx install memor-cli

# Install the hook + download embedding model (~60MB)
memor install-hook                  # interactive — pick an agent
memor install-hook --agent kimi     # or pass directly (claude, codex, copilot, kimi, goose)

# Start as a background service (macOS/Linux)
memor service install

# Or run in the foreground
memor daemon

That's it. Every prompt now gets automatic context recall. memor service install also starts the dashboard as a background service, so it's already live at http://localhost:8420 (and is recycled whenever you stop/restart/uninstall the service). To run it in the foreground instead:

memor dashboard
# Opens http://localhost:8420

Alternative install: pip install memor-cli works too — just make sure ~/.local/bin is on your PATH so the memor command is available.

Optional: Token savings proxy

Memory works out of the box via hooks. To also compress tool payloads and track token savings (Claude Code or Codex only):

memor install-proxy --agent claude   # or: codex

This points your agent at a local proxy on 127.0.0.1:8421, compresses latest-turn tool content before it reaches the provider, and logs savings to the dashboard. Hooks keep running for that agent, so memory works exactly as before. Cursor and Copilot always use hooks only.

To revert: memor uninstall-proxy --agent claude

Codex support is experimental. The proxy implements the OpenAI Chat Completions API (/v1/chat/completions). Codex CLI may instead use the Responses API (/v1/responses) depending on version and model, in which case requests will not route through the proxy and you will see no savings. Memory via hooks is unaffected. Track it in #26.

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Dual-Path Architecture

Memor runs two complementary local paths — combine them or use either alone:

                         ┌──────────────────────────────────────┐
                         │        Memor (localhost)             │
                         │  daemon · hooks · proxy · dashboard  │
                         └──────────────────┬───────────────────┘
                                            │
              ┌─────────────────────────────┼─────────────────────────────┐
              ▼                             ▼                             ▼
       Path A: Hooks                 Path B: Proxy                 Shared store
       (memory, all agents)          (savings, opt-in)             SQLite + vec
              │                             │                      + FTS + ledger
              │ recall inject               │ compress latest-turn
              │ always on after             │ tool payloads; forward
              │ install-hook                │ to provider; CCR originals
              ▼                             ▼
       Claude · Cursor · Codex         Anthropic / OpenAI
       Copilot · Kimi · Goose          (your existing credentials)
              ▲
              │
       Daemon ingests sessions ── Claude ~/.claude/projects/
                                  Kimi   ~/.kimi/sessions/
                                  Goose  ~/.local/share/goose/...

Path	Purpose	Default
Hooks	Shared memory recall across all agents	On after memor install-hook
Proxy	Compress tool payloads; ledger token savings	Opt-in via memor install-proxy

Memory is fire-and-forget — install hooks once, every prompt gets relevant context. Proxy is opt-in — only for Claude Code and Codex, only when you want measurable token savings. Hooks stay in charge of memory even when the proxy is active (proxy-side inject is best-effort). The proxy forwards your existing Anthropic/OpenAI credentials; Memor does not require its own API key.

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How It Works (hooks path)

  You type a prompt
  (Claude · Cursor · Codex · Copilot · Kimi · Goose)
      |
      v
  Hook fires — auto-detects which agent
      |
      v
  Embed query locally (model2vec, ~2ms)
      |
      v
  Hybrid retrieval: dense vectors + lexical BM25, fused (RRF)
      |
      v
  Relevance gate drops off-topic matches (inject nothing if nothing fits)
      |
      v
  Rank: similarity + recency + kind weight + quality
      |
      v
  Inject relevant context into prompt
      |
      v
  Your agent sees past decisions, bugfixes,
  architecture choices — without you re-explaining

Agent matrix

Agent	Memory (hooks)	Proxy / savings
Claude Code	Yes	Yes — memor install-proxy --agent claude
Codex CLI	Yes	Experimental — memor install-proxy --agent codex (Chat Completions only)
Cursor	Yes	No — hooks only
Copilot CLI	Yes	No — hooks only
Kimi CLI	Yes	No — hooks only
Goose	Yes	No — hooks only

Hook install details

Agent	Hook protocol	Config location	Install
Claude Code	UserPromptSubmit + additionalContext	~/.claude/settings.json	memor install-hook --agent claude
Codex CLI	UserPromptSubmit + additionalContext	~/.codex/hooks/hooks.json	memor install-hook --agent codex
Copilot CLI	userPromptSubmitted + additionalContext	~/.copilot/hooks/memor.json	memor install-hook --agent copilot
Cursor	beforeSubmitPrompt + additionalContext	~/.claude/settings.json (loaded as Claude user hooks)	automatic — covered by the Claude install
Kimi CLI	UserPromptSubmit + plain-text context	~/.kimi/config.toml	memor install-hook --agent kimi
Goose	UserPromptSubmit + additionalContext	~/.agents/plugins/memor/	memor install-hook --agent goose

A single memor-hook binary auto-detects which agent is calling it — no separate entry points needed. Kimi and Goose installs stamp MEMOR_HOOK_AGENT so Claude-shaped payloads stay correctly labeled. Cursor loads the same Claude user hooks, so installing for Claude Code covers Cursor too. Hooks stay in charge of memory inject even when the proxy is active; the proxy's own inject is best-effort, because it cannot see your working directory to scope the project. The dashboard tracks recalls per agent so you can see usage across all your environments.

Goose note: Memory inject needs a Goose build with advise-tier additionalContext support. DeepSeek (or any other provider) is configured inside Goose — Memor talks to Goose's hooks, not to the model provider.

Note: Cloud-hosted agents (Codex cloud, Copilot cloud agent) run in remote sandboxes and cannot reach local hooks. MCP server support for sandboxed agents is planned (#26).

Background processes (supervised by memor service install):

1. Daemon — polls local agent session stores (Claude Code ~/.claude/projects/, Kimi ~/.kimi/sessions/, Goose ~/.local/share/goose/sessions/sessions.db), embeds chunks, runs distillation, analyzes feedback (Claude), promotes cross-project patterns to global scope, compacts duplicates, auto-compacts the vector index when bloated, tracks session-level token usage. Model providers are not ingest sources — only the agent that owns the session. All local. Use memor backfill for a one-shot ingest of past sessions.
2. Hook — fires on every prompt, recalls relevant memories, injects them as context. Sub-15ms. Works across Claude Code, Cursor, Codex, Copilot, Kimi, and Goose.
3. Proxy (optional) — intercepts Anthropic/OpenAI API calls on 127.0.0.1:8421, compresses latest-turn tool payloads, forwards to your provider, and writes a savings ledger. Started automatically by memor install-proxy.

No Memor API key required. Embeddings and compressors run locally. The proxy forwards your existing Anthropic/OpenAI credentials — keys are never stored. Vectors stored in sqlite-vec. Everything runs on your machine.

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Hybrid Retrieval

Memor retrieves over two channels and fuses them, so it catches both semantic matches and exact terms:

• Dense — local vector similarity (model2vec) for semantic recall.
• Lexical — SQLite FTS5 / BM25 over the raw text, to recover exact identifiers, error strings, and API names that static embeddings blur together.

The two rankings are combined with Reciprocal Rank Fusion (RRF). A relevance gate drops anti-correlated (off-topic) candidates before ranking, so an unrelated prompt injects nothing rather than the least-bad guess. The lexical channel only activates when the dense channel finds the query on-topic, preventing generic words from pulling in noise.

Tunable via MEMOR_MIN_SIMILARITY (relevance floor, default 0.0) and MEMOR_MAX_TOKENS (injection budget, default 1500).

Scoring

Surviving candidates are ranked by four signals:

Signal	Weight	How it works
Semantic similarity	50%	Dense + lexical relevance, fused via RRF
Recency	25%	Exponential decay with 14-day half-life — recent decisions rank higher
Kind weight	15%	Distilled memories (1.3x) rank above raw session chunks (1.0x)
Quality	10%	Bayesian score from implicit feedback — memories the agent actually uses rank higher

This means a relevant decision from yesterday beats a vaguely-related chunk from a month ago — even if the raw embedding similarity is similar.

Feedback Loop

Memor tracks whether recalled memories actually get used by the agent — and whether they actively hurt. After each session, the daemon analyzes the transcript in both directions:

• Positive signal — n-gram overlap or semantic similarity between recalled content and the agent's response. Memories that consistently prove useful get quality boosts.
• Negative signal — user rejection ("no that's wrong", "we switched to X") or assistant contradiction ("however, looking at the current code, we actually use Y"). Memories that get corrected receive a quality penalty, making them less likely to be recalled next time.

The quality formula is Bayesian: (uses - negatives + 1) / (recalls + 2). One correction weighs as much as one positive use, so harmful memories drop fast. Memories never recalled in 30+ days get automatically deactivated. Near-duplicate memories are compacted into one.

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What Gets Stored

Kind	Source	Description
session_chunk	Daemon auto-ingest	Filtered turns from Claude / Kimi / Goose sessions
memory	Extractive distillation	Key decisions, patterns, bugfixes per session

Memories are automatically classified as decision, bugfix, lesson, snippet, or generic extract based on content patterns. The daemon runs a signal filter that keeps decisions, bugfixes, lessons, and code rationale while skipping noise (tool calls, file listings, boilerplate).

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Global Memories

Some patterns aren't project-specific — they're yours. "Always use type hints." "Structure FastAPI apps with a routes/ directory." "Prefer composition over inheritance."

Memor detects these automatically. When the same pattern appears in 3 or more projects (measured by embedding similarity), the daemon promotes it to a _global scope:

• Global memories are recalled everywhere — they show up in every project's search results alongside project-specific memories.
• Source duplicates are deactivated — the per-project copies get superseded by the single global version, reducing clutter.
• No manual tagging — promotion is fully automatic, based on cross-project clustering.

This means your coding habits and preferences follow you into new projects from the first prompt, without you having to re-explain anything.

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Dashboard

memor dashboard

Dark fintech-inspired UI showing:

• System Status — metric cards for proxy / hook / daemon / proxied agents, plus token savings and compressed content types when the ledger has data
• Memory Bank — session chunks, memories, projects, globals, total recalls — with sparkline bars
• Agent breakdown — per-agent recall stats (Claude, Cursor, Codex, Copilot, Kimi, Goose) with hit rates
• Daily recall activity — stacked bar chart of hits vs misses over time
• Session efficiency — real token savings measured from API usage data (avg tokens/turn with vs without recall)
• Per-project breakdown — artifact counts, token totals, last activity
• Recent recalls — every hook event with agent badge, scores, latency, and status

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Local distillation (optional, no API key)

memor can distill sessions with a small local model (offline, in-process, ingest-only — recall never uses an LLM). Enable it:

pip install "memor-cli[llm]"   # or: pip install "llama-cpp-python>=0.3.0"
export MEMOR_LLM_DISTILL=1
memor daemon

On first run it downloads ~1.1 GB (Qwen2.5-1.5B GGUF, Apache-2.0), cached thereafter. On CPUs without AVX2, or if the wheel can't build, install a prebuilt CPU wheel:

pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu

If the model is unavailable, distillation falls back to extractive mode automatically.

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Commands

memor help                           Print the full manual
memor install-hook                   Install hook + download model (interactive agent picker)
  --agent claude|codex|copilot|kimi|goose   Choose agent directly
memor install-proxy                  Install local proxy for token savings
  --agent claude|codex               Claude Code, or Codex CLI (experimental)
memor uninstall-proxy                Restore original agent config
  --agent claude|codex               Point the agent back at its provider
memor proxy                          Run proxy server in foreground (localhost:8421)
memor daemon                         Auto-ingest + distill (Claude, Kimi, Goose)
memor backfill                       One-shot ingest of past local agent sessions
memor dashboard                      Web dashboard on localhost:8420
memor version                        Print installed version
memor service install                Run daemon + dashboard as background services (launchd/systemd)
  --no-dashboard                     Install only the daemon
memor service restart                Restart both services (use after `pipx upgrade`)
memor service stop                   Stop both background services
memor service uninstall              Remove both background services
memor service status                 Show daemon + dashboard status
  (dashboard port: set MEMOR_DASHBOARD_PORT, default 8420)
memor query <text>                   Search memories from the CLI
memor reingest                       Wipe DB and re-ingest everything
memor reingest --project <name>      Re-ingest only one project
memor forget-stale                   Deactivate memories unused for 30+ days
memor compact                        Rebuild vector index, reclaim wasted space
memor scan                           Audit DB for leaked secrets
memor scan --purge                   Redact secrets in place
memor setup-model                    Download/retry the embedding model
memor ingest-cc <file>               Ingest a single transcript
memor ingest-project <dir>           Bulk ingest a project directory
memor ingest-doc <file>              Ingest a markdown document
memor distill --project <name>       Run distillation manually
memor eval <cases.json>              Run eval suite
memor eval-counterfactual --project  Win/tie/loss vs no-memory baseline
memor bench-embed --project <name>   Compare embedding models

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Architecture

memor/
+-- types.py / interfaces.py   Core types + Embedder/LLM/MemoryStore protocols
+-- cli.py                     Typer CLI (hooks, proxy, daemon, eval, service)
+-- daemon.py                  Multi-agent ingest + distill + compaction
+-- recall.py                  Shared recall core (hook + skill + proxy inject)
+-- service.py                 launchd/systemd: daemon + dashboard (+ proxy)
+-- redact.py / feedback.py    Secret redaction; positive/negative quality loop
+-- global_memories.py         Cross-project promotion to _global scope
|
+-- ingest/
|   +-- claude_code.py         ~/.claude/projects/ JSONL
|   +-- kimi.py                ~/.kimi/sessions/ wire.jsonl
|   +-- goose.py               Goose sessions.db
|   +-- sources.py             Registry used by daemon + backfill
|
+-- hook_cli.py / hook_server.py
|                              Hook entry + agent detect/format
|                              (Claude, Cursor, Codex, Copilot, Kimi, Goose)
|
+-- proxy/                     Opt-in token-savings path (localhost:8421)
|   +-- server.py / pipeline.py  Compress → forward → ledger
|   +-- install.py               Wire agent config + backups
|   +-- mcp_retrieve.py          memor_retrieve MCP tool
|
+-- compress/                  Log / search / JSON crushers for tool payloads
|
+-- retrieve/retriever.py      Hybrid dense + BM25 (RRF) + relevance gate
+-- store/sqlite_store.py      SQLite + sqlite-vec + FTS5 + proxy_savings
|
+-- dashboard/                 FastAPI + static UI (status, savings, agents)
+-- distill/                   Extractive default; optional local GGUF LLM
+-- embed/                     model2vec local (default) + API/fake
+-- eval/                      Counterfactual, proxy benchmark fixtures, baselines
+-- llm/                       Anthropic / OpenAI-compat / llama.cpp backends

skill/recall.py                Standalone recall script

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Security

Nothing leaves your machine. In the default configuration (hooks only, no proxy):

• No telemetry, no analytics, no phone-home. Memor itself makes zero outbound network calls.
• Embeddings run locally via model2vec static token embeddings — no inference runtime, no GPU (one-time model download from HuggingFace — no user data sent).
• Hook transport is a Unix socket (~/.memor/hook.sock), not a network port.
• Dashboard binds localhost only (127.0.0.1:8420).

With the proxy enabled, Memor is on the wire. memor install-proxy puts Memor in the path of every request your agent makes to Anthropic or OpenAI:

• The proxy binds localhost only (127.0.0.1:8421) and accepts no remote connections.
• It makes the outbound call your agent would have made anyway, to the same provider endpoint, carrying your existing provider API key. Keys are forwarded, never stored or logged.
• It rewrites request bodies — compressing latest-turn tool payloads — so what the provider receives is not byte-identical to what your agent sent. Originals stay local in the CCR store.

The only other optional network paths are the LLM-based abstractive distiller (requires explicitly setting ANTHROPIC_API_KEY) and the API embedding backend — both off by default.

Secret redaction

Memor automatically redacts secrets at ingest, before anything is embedded or stored:

• API keys (AWS AKIA..., OpenAI sk-..., Anthropic sk-ant-..., GitHub ghp_..., Stripe, Slack)
• JWTs, PEM private key blocks
• Connection strings (postgres://, mongodb://, redis://, etc.)
• .env-style assignments (DB_PASSWORD=..., API_KEY=...)
• High-entropy tokens (Shannon entropy > 4.0, length > 20)

Redacted content is replaced with [REDACTED] in place, preserving surrounding context. To audit and clean an existing database: memor scan (audit) or memor scan --purge (redact in place).

Contradiction handling

When a new memory contradicts an older one in the same project (detected via replacement cues like "switched from X to Y", "no longer", "ripped out"), the older memory is automatically deactivated. This prevents stale decisions from being recalled and misleading the agent.

Local storage

The memory database (~/.memor/memor.db) is stored as plaintext SQLite on disk. For at-rest protection, we recommend enabling OS-level full-disk encryption (FileVault on macOS, LUKS on Linux) which covers all local files with zero performance overhead.

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Development

git clone https://github.com/bnimit/memor-ai.git
cd memor-ai
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

pytest  # 445 tests

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License

MIT. See LICENSE for the full text.